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Citation impact AI simulator

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Citation impact

Citation impact or citation rate is a measure of how many times an academic journal article or book or author is cited by other articles, books or authors. Citation counts are interpreted as measures of the impact or influence of academic work and have given rise to the field of bibliometrics or scientometrics, specializing in the study of patterns of academic impact through citation analysis. The importance of journals can be measured by the average citation rate, the ratio of number of citations to number articles published within a given time period and in a given index, such as the journal impact factor or the citescore. It is used by academic institutions in decisions about academic tenure, promotion and hiring, and hence also used by authors in deciding which journal to publish in. Citation-like measures are also used in other fields that do ranking, such as Google's PageRank algorithm, software metrics, college and university rankings, and business performance indicators.

One of the most basic citation metrics is how often an article was cited in other articles, books, or other sources (such as theses). Citation rates are heavily dependent on the discipline and the number of people working in that area. For instance, many more scientists work in neuroscience than in mathematics, and neuroscientists publish more papers than mathematicians, hence neuroscience papers are much more often cited than papers in mathematics. Similarly, review papers are more often cited than regular research papers because they summarize results from many papers. This may also be the reason why papers with shorter titles get more citations, given that they are usually covering a broader area.

The most-cited paper in history is a paper by Oliver Lowry describing an assay to measure the concentration of proteins. By 2014 it had accumulated more than 305,000 citations. The 10 most cited papers all had more than 40,000 citations. To reach the top-100 papers required 12,119 citations by 2014. Of Thomson Reuter's Web of Science database with more than 58 million items only 14,499 papers (~0.026%) had more than 1,000 citations in 2014.

Total citations, or average citation count per article, can be reported for an individual author or researcher. Many other measures have been proposed, beyond simple citation counts, to better quantify an individual scholar's citation impact. The best-known author-level measures include total citations and the h-index. Each measure has advantages and disadvantages, spanning from bias to discipline-dependence and limitations of the citation data source. Counting the number of citations per paper is also employed to identify the authors of citation classics.

Citations are distributed highly unequally among researchers. In a study based on the Web of Science database across 118 scientific disciplines, the top 1% most-cited authors accounted for 21% of all citations. Between 2000 and 2015, the proportion of citations that went to this elite group grew from 14% to 21%. The highest concentrations of 'citation elite' researchers were in the Netherlands, the United Kingdom, Switzerland and Belgium. 70% of the authors in the Web of Science database have fewer than 5 publications, so that the most-cited authors among the 4 million included in this study constitute a tiny fraction.

The simplest journal-level metric is the journal impact factor, the average number of citations that articles published by a journal in the previous two years have received in the current year, as calculated by Clarivate. Other companies report similar metrics, such as the CiteScore, based on Scopus.

However, very high journal impact factor or CiteScore are often based on a small number of very highly cited papers. For instance, most papers in Nature (impact factor 38.1, 2016) were only cited 10 or 20 times during the reference year (see figure). Journals with a lower impact (e.g. PLOS ONE, impact factor 3.1) publish many papers that are cited 0 to 5 times but few highly cited articles.

Journal-level metrics are often misinterpreted as a measure for journal quality or article quality. However, the use of non-article-level metrics to determine the impact of a single article is statistically invalid. Moreover, studies of methodological quality and reliability have found that "reliability of published research works in several fields may be decreasing with increasing journal rank", contrary to widespread expectations.

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